In [1]:
!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install --upgrade plotly
!pip install lxml

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In [2]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup 
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [3]:
import plotly.io as pio
pio.renderers.default = "iframe"
In [4]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)
In [5]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
    from IPython.display import display, HTML
    fig_html = fig.to_html()
    display(HTML(fig_html))

Question 1: Use yfinance to Extract Stock Data

In [6]:
tesla=yf.Ticker('TSLA')
In [7]:
tesla_data = tesla.history(period='max')
In [8]:
tesla_data.reset_index(inplace=True)
tesla_data.head(5)
Out[8]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data

In [9]:
URL=('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm')
html_data=requests.get(URL).text
In [10]:
soup = BeautifulSoup(html_data, 'html.parser')
In [11]:
tables=pd.read_html(str(soup))
print(f'number of tables found:{len(tables)}')
Tesla_Revenue=tables[1]
Tesla_Revenue.columns =['Date','Revenue']

number of tables found:6
In [12]:
Tesla_Revenue["Revenue"] = Tesla_Revenue['Revenue'].str.replace(',|\$',"",regex=True)
In [13]:
Tesla_Revenue.dropna(inplace=True)

Tesla_Revenue = Tesla_Revenue[Tesla_Revenue['Revenue'] != ""]
In [14]:
Tesla_Revenue.tail(5)
Out[14]:
Date Revenue
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
52 2009-09-30 46
53 2009-06-30 27

Question 3: Use yfinance to Extract Stock Data

In [15]:
A=yf.Ticker("GME")
In [16]:
gme_data=A.history(period='max')
In [17]:
gme_data.reset_index(inplace=True)
gme_data.head()
Out[17]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620129 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716074 1.670626 1.683250 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615921 1.662210 1.603296 1.662210 6892800 0.0 0.0

Question 4: Use Webscraping to Extract GME Revenue Data

In [18]:
url1=("https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html")
html_data_2=requests.get(url1).text
In [19]:
soup1 = BeautifulSoup(html_data_2,'html.parser')
In [25]:
gme_table=pd.read_html(str(soup1))
print(f'Number of tables found:{len(gme_table)}')
gme_revenue=gme_table[1]
gme_revenue.columns=['Date','Revenue']
gme_revenue['Revenue']=gme_revenue['Revenue'].str.replace(',|\$','',regex=True)
gme_revenue.dropna(inplace=True)
gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]
gme_revenue.reset_index(drop=True,inplace=True)
print(gme_revenue.head())

Number of tables found:6
         Date Revenue
0  2020-04-30    1021
1  2020-01-31    2194
2  2019-10-31    1439
3  2019-07-31    1286
4  2019-04-30    1548
In [26]:
gme_revenue.tail(5)
Out[26]:
Date Revenue
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709

Question 5: Plot Tesla Stock Graph

In [27]:
make_graph(tesla_data, Tesla_Revenue, 'Tesla')

Question 6: Plot GameStop Stock Graph

In [28]:
make_graph(gme_data, gme_revenue, 'GameStop')
In [ ]: